Estimation of Tsunami Characteristics from Deposits: Inverse Modeling Using a Deep‐Learning Neural Network

Tsunami deposits provide information for estimating the magnitude and flow conditions of paleotsunamis, and inverse models have potential for reconstructing hydraulic conditions of tsunamis from their deposits. The majority of the previously proposed models are based on oversimplified assumptions and possess some limitations. We present a new inverse model based on the FITTNUSS model, which incorporates nonuniform and unsteady transport of suspended sediment and turbulent mixing. The present model uses a deep neural network (DNN) for the inversion method. In this method, forward model calculations are repeated for random initial flow conditions (e.g., maximum inundation length, flow velocity, maximum flow depth, and sediment concentration) to produce artificial training data sets of depositional characteristics such as thickness and grain‐size distribution. The DNN was then trained to establish a general inverse model based on artificial data sets derived from the forward model. Tests conducted using independent artificial data sets indicated that this trained DNN can reconstruct the original flow conditions from the characteristics of the deposits. Finally, the model was applied to a data set of 2011 Tohoku‐oki tsunami deposits. The predicted results of flow conditions were verified by the observational records at Sendai plain. Jackknife resampling was applied to estimate the precision of the result. The estimated results of the flow velocity and maximum flow depth were approximately 5.4 ± 0.1 m/s and 4.1 ± 0.2 m, respectively, after the uncertainty analysis. The DNN shows promise for reconstruction of tsunami characteristics from its deposits, which would help in estimating the hydraulic conditions of paleotsunamis. Plain Language Summary This study presents an inverse model that uses an artificial intelligence technique to estimate the hydraulic conditions of paleotsunamis from deposits. The estimated flow conditions are essential tools for disaster resilience and tsunami hazard mitigation to reduce socioeconomic impact of tsunamis on coastal cities. Key Points Inverse modeling of paleotsunami deposits was performed using deep‐learning neural network 2011 Tohoku‐oki tsunami's flow velocity, maximum depth, inundation length, and sediment concentration were evaluated with inverse model Comparison of observations and uncertainty analysis implied that the reconstructed flow conditions were accurate and reasonably precise